Based upon the analysis of load signatures, this paper presents a Non-intrusive load monitoring (NILM) technique. With characterizing associated with the transient response of energy signature, a reliable and accurate recognition result can be obtained. In this study, artificial neural networks (ANN), in combination with turn-on transient energy analysis, are used to improve recognition accuracy and computational speed of NILM results. To minimize the distortion phenomenon in current measurements from the hysteresis of traditional current transformers (CTs) iron cores, coreless Hall effect current transformer is adopted to accurately detect non sinusoidal waves to improve NILM accuracy. The experimental results indicate that the incorporation of turn-on transient energy algorithm into NILM significantly improve the recognition accuracy and computational speed.
Summary
Owing to increased capabilities of power quality monitors, synchronized harmonic phasor data are becoming more widely available. Taking advantage of the new data, this paper presents a new and effective method to solve the problem of how to estimate the harmonic impact of several individual loads on the harmonic voltages at a specific location of a power network. The method uses the independent fluctuation of the harmonic sources and is solved as a blind source separation problem. The proposed method has been verified through simulation verification where harmonic currents measured at actual substations are used as input so realistic load fluctuations are considered. Furthermore, lab experiments are conducted to validate the proposed method.
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